Abstract
In Chapter 1 it was stated that transport demand flows result from the aggregation of individual trips. Each trip is the result of several choices made by the users: travelers in passenger transportation or operators (manufacturers, shippers, carriers) in goods transport. Some traveler choices are made infrequently, such as where to reside and work and whether to own a vehicle or not. Other choices are made for each trip, these include whether to make a trip for a certain purpose at what time to what destination, with what mode, using what route. Each choice context, defined by available alternatives, evaluation factors and decision procedures, is usually known as a “choice dimension”. Also, in most cases, choices concerning transport demand are made among a finite number of discrete alternatives.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Reference Notes
Domencich T. A., and D. McFadden (1975). Urban travel demand: a behavioural analysis. American Elsevier, New York.
Williams H.C.W.L. (1977). On the formation of travel demand models and economic evaluation measures of user benefit Environment and Planning A: 285–344.
Manski C. (1977). The structure of random utility models Theory and Decision 8: 229–254.
Manski C.F., and D. McFadden (1981). Alternative estimators and sample designs for discrete choice analysis. in Structural Analysis of discrete data with Econometric Applications, MIT Press, Cambridge, Mass.
Ben-Akiva M., and S. Lerman (1985). Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press, Cambridge, Mass.
Williams H.C.W.L., and J. de D. Ortùzar (1982). Behavioural theories of dispersion and the mis-specification of travel demand models. Transportation Research 16B: 167–219.
Horowitz J.L. (1985). Travel and location behaviour: state of the art and research opportunities Transportation Research 19A: 441–454.
Bath C. (1997). Recent Methodological Advances Relevant to Activity and Travel Behavior Analysis. Proceedings of the V III IATBR conference, Resource papers, Austin, Texas.
Daly A.J., and S. Zachary (1978), Improved multiple choice models. in D.A. Hensher and M.Q. Dalvi (eds.), Determinants of Travel Choice. Saxon House, Westmead.
Daganzo C.F., and M. Kusnic (1992). Another look at the nested logit model. Technical Report UCB-ITS-RTR 92–2, Institute of Transportation Studies, University of California, Berkeley.
McFadden, D. (1978). Modeling the choice of residential location. in A.K.(ed.), Spatial interaction theory and residential location: 75–96, North-Holland, Amsterdam.
Small, and K. (1987). A discrete choice model for ordered alternatives Econometrica 55: 409–424.
Vovsha P. (1997). The Cross-Nested Logit Model: Application to Mode Choice in the Tel-Aviv Metropolitan Area Proceedings of the 76`h TRB Meeting.
Vovsha P. S. Bekor (1998). The link-Nested Logit Model of Route Choice: Overcoming the Route Overlapping Problem Proceedings of the 77th TRB Meeting.
Cascetta E., and A. Papola (2000). Implicit availability/perception models for the simulation of travel demand Transportation Research C, forthcoming.
Ben-Akiva M., and B. Francois (1983). p Homogeneous Generalized Extreme Value Model. Working paper, Department of Civil Engineering, MIT Cambridge, Mass.
Papola A. (1996). I modelli di Valore Estremo Generalizzato (GEV) per la simulazione della domanda di trasporto. Internal report. Department of Transportation Engineering, University of Naples “Federico II”.
Daganzo C.F. (1979). Multinomial probit: the theory and its application to demand forecasting. Academic press, New York.
Dafermos S.C. (1982). The general multimodal network equilibrium problem with elastic demand Networks 12: 57–72.
Langdon M.G. (1984). Improved algorithms for estimating choice probabilities in the multinomial probit model Transportation Science 18: 267–299.
Ben-Akiva, M., and M. Bierlaire (1999). Discrete choice methods and their application to short term travel decisions. Handbook of Transportation Science, R.W. Hall ed., Kluwer Academic Publishers: 5–33.
Ortuzar J.de D., and L.G. Willumsen (1994). Modelling Transport John Wiley and Sons, 2nd edition.
Ben-Akiva M., and D. Bolduc (1996). Multinomial Probit with a Logit Kernel and a General Parametric Specification of the Covariance Structure. Working Paper, Department of Economics, MIT, Boston, Mass.
Bolduc, D., B. Fortin, and M.A. Fournier (1996). “The Impact of Incentive Policies to Influence Practice Location of General Practitioners: A Multinomial Probit Analysis”, Journal of Labor Economics 14: 703–732.
Ben-Akiva M., and B. Boccara (1995). Discrete choice models with latent choice sets. International Journal of Research in Marketing 12: 9–24.
Cascetta E., and A. Papota (2000). A joint mode-run choice model to simulate the schedule influence at a regional level. Proceedings of the 9th IATBR conference, Sidney, Australia.
Daganzo C.F. (1979). Multinomial probit: the theory and its application to demand forecasting. Academic press, New York.
Cantarella G.E. (1997). A General Fixed-Point Approach to Multi-Mode Multi-User Equilibrium Assignment with Elastic Demand Transportation Science 31: 107–128.
Koppelman F.S. (1989). Multidimensional Model System for Intercity Travel Choice Behaviour Transportation Research Records 1241: 1–8.
Ben-Akiva M., and T. Atherton (1977). Methodology for Short-Range Travel Demand Predictions Journal of Transport Economics and Policy 11: 224–261.
Watanatada T., and M. Ben-Akiva (1979). Forecasting urban travel demand for quick policy analysis with disaggregate choice model: a Monte-Carlo simulation approach. Transportation Research 13A: 241–248.
Gunn H., J. and J.J. Bates (1982). Statistical aspects of travel demand modelling. Transportation Research 16A: 371–382.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Cascetta, E. (2001). Random Utility Theory. In: Transportation Systems Engineering: Theory and Methods. Applied Optimization, vol 49. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-6873-2_3
Download citation
DOI: https://doi.org/10.1007/978-1-4757-6873-2_3
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4757-6875-6
Online ISBN: 978-1-4757-6873-2
eBook Packages: Springer Book Archive